{"id":"W4408048039","doi":"10.1109/tvt.2025.3546717","title":"Transfer of Reinforcement Learning-Based Powertrain Controllers From Model- to Hardware-in-the-Loop","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Real-time simulation and control systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Bundesministerium für Wirtschaft und Klimaschutz","keywords":"Powertrain; Control engineering; Reinforcement learning; Transfer function; Computer science; Loop (graph theory); Engineering; Hardware-in-the-loop simulation; Artificial intelligence; Electrical engineering; Torque; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004722889,0.0004624409,0.0002998667,0.0001541137,0.0001757792,0.0004408682,0.0007526829,0.0003548597,0.002261722],"category_scores_gemma":[0.002179198,0.0002167833,0.0002759447,0.00008015646,0.0005049172,0.0004985549,0.0006070672,0.0007559785,0.0003642979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000429294,"about_ca_system_score_gemma":0.0006502663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003329215,"about_ca_topic_score_gemma":0.001376145,"domain_scores_codex":[0.9998578,0.00003428708,0.000007591163,0.00002804733,0.00004539264,0.00002680103],"domain_scores_gemma":[0.9995773,0.0001910037,0.00005853201,0.00007365515,0.00007768932,0.00002175188],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003242316,0.00006685851,0.0003939018,0.00002347854,0.00001051705,0.00003181995,0.00003936245,0.9812683,0.002279745,0.001260142,0.000193193,0.01440027],"study_design_scores_gemma":[0.000008106365,0.00004361572,0.00007208765,0.000002526983,0.000002261581,0.000004839941,0.000004146345,0.9972396,0.001815843,0.0005602479,0.0002446922,0.000002138069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1888924,0.0000840022,0.7957116,0.0001864444,0.00006211348,0.0002279244,0.00004462422,0.002206078,0.01258489],"genre_scores_gemma":[0.9701399,0.00003175914,0.02823771,0.00004285708,0.000005511684,0.000100295,0.00003497292,0.00004493011,0.001361959],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003329215,"threshold_uncertainty_score":0.007566214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007427316967612867,"score_gpt":0.2225570112608752,"score_spread":0.2151296942932623,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}